The main contribution of our work relies on the fact that we regularize and prune the parameters in a CapsNet as a way to minimize the entropy of the connections computed by the routing algorithm. In fact, we show that relationships between objects and their parts in a standard CapsNets described by Sabour et al. [1]} have high entropies. We minimize these entropies so that we can extract stronger parse trees. This allows us to effectively build dictionaries upon the input datasets and understand which are the shared object parts and transformations between different the entities in the images.
